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<p>Effective solid waste classification is crucial for efficient waste management and environmental sustainability. Addressing this challenge is essential for improving urban quality of life. This study develops an accurate and efficient model for automatically classifying various types of solid waste. We leverage a large, annotated dataset of waste objects from diverse sources and apply state-of-theart deep learning techniques, including advanced architectures such as Xception, DenseNet, and EfficientNet. While previous studies have used computer vision for waste classification, our approach demonstrates the significant effectiveness of deep learning methods in solving this problem. By improving classification accuracy, our model aims to optimize waste management practices, contributing to a cleaner and more sustainable urban environment.</p>

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Solid waste classification using deep neural network: A transfer learning approach

  • 期刊论文

  • 发表日期：

    2025年2月14日

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  • Parita Oza,
  • Smita Agrawal,
  • Mayank Kapadia,
  • Omkar Raotole

摘要

Effective solid waste classification is crucial for efficient waste management and environmental sustainability. Addressing this challenge is essential for improving urban quality of life. This study develops an accurate and efficient model for automatically classifying various types of solid waste. We leverage a large, annotated dataset of waste objects from diverse sources and apply state-of-theart deep learning techniques, including advanced architectures such as Xception, DenseNet, and EfficientNet. While previous studies have used computer vision for waste classification, our approach demonstrates the significant effectiveness of deep learning methods in solving this problem. By improving classification accuracy, our model aims to optimize waste management practices, contributing to a cleaner and more sustainable urban environment.